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analytics, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design, probability theory
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residency requirement, you will be required to obtain a PIV credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and
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deep learning models using the Oak Ridge Leadership Computing Facility (OLCF) systems. Conduct research with scalable transformer-based foundation models with large volumes of spatiotemporal physical
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for characterization and analysis of membranes and composites including X-ray/neutron powder diffraction, electron microscopy and lithium analysis using ICP, NMR, etc. Collaborate with ORNL postdocs and staff who
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offered a salary at or near the top of the range for a position. Link to benefits. https://jobs.ornl.gov/content/Benefits/?locale=en_US Overview: We are seeking a Machine Learning Research Scientist who
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ready. Integrate grid-edge measurements and system-level observations into unified workflows for DER aggregation and parameter identification. Apply machine learning techniques to improve DER/IBR model
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credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting
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computational thermodynamic (CALPHAD) software, such as Thermo-Calc, DICTRA, PANDAT, or FactSage. Proficiency in materials data analytics, including correlation analysis and machine learning techniques. Preferred
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of AI for science, including scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models on leadership-class supercomputers. You’ll help design